As is known, increasing the spatial resolution together with the dynamic range of PIV measurements is one of the goals of recent efforts in PIV development. One way to achieve high spatial resolution without limiting the dynamic range is to apply a hybrid processing algorithm including sub-image correlation followed by particle tracking. Such methods aim to circumvent problems of conventional block correlation that are expressed by the well-known one-quarter rule of selecting interrogation area size relative to the largest measurable displacement. However, correlation has the potential to resolve particle movement at a much smaller scale than indicated by the above constraint if it is used in a hierarchical coarse-to-fine algorithm. Resolving the particle movement at a relatively small scale involves reducing the interrogation area size in several steps iteratively starting from a coarse spatial resolution where high signal-to-noise ratio ensures high probability of valid correlation peak detection.
One conventional hierarchical processing algorithm is commonly referred to as a Laplacian pyramid processing algorithm, which is widely used in computer vision for multi-resolution image processing and analysis. The main feature of this algorithm is that it resolves larger and smaller scales of fluid motion, for example, at different levels of the processing based on sub-sampled band-limited images. The multi-resolution feature of such an algorithm allows fast processing as well as increased spatial resolution.
Other coarse-to-fine processing algorithms are commonly referred to as a recursive local-correlation, which provides increased spatial resolution by recursively correlating the image frames at finer and finer mesh sizes down to the size of an individual particle image. Correlation search length is also reduced iteratively to the smallest meaningful scale parallel with the decrease of interrogation area size. This process in connection with the sparse array image correlation that is applied directly in the spatial domain makes this processing faster than its Fast Fourier Transform (FFT) based equivalent at usual flow and imaging parameters.
While these algorithms are may work, they are not generally computationally efficient considering that correlation values for certain pixels are repeatedly calculated along with refining the mesh of computation. Furthermore, there is no efficient way to set the spatial resolution for the top level of the hierarchy for the above-described hierarchical processing algorithms. It is likely that different flow regions of a hierarchical image of a fluid under test require different initial interrogation area sizes that ensure valid detection when no prior knowledge of the flow field is available.
Another drawback of the above-described hierarchical processing algorithms relates to when the bottom level of the hierarchical image of the fluid under test contains strongly inhomogeneous flow regions. Interrogation areas at this smallest scale might not contain enough particle images, particularly in sparsely seeded flow areas (e.g. vortex core, flow separation region etc.) to obtain valid information. In addition to these shortcomings, optimizing interrogation areas with respect to their signal content becomes difficult due to the top-to-bottom processing path.
Furthermore, current temporal estimates of statistical flow parameters may be too restrictive in terms of highest resolvable spatial scale due to the need of recovering instantaneous flow velocities from image pairs, or strongly biased due to improper combination of correlation data, for example by ensemble averaging correlation planes.
It would, therefore, be desirable to overcome the aforesaid and other disadvantages.